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\title{Raven: A Data-Driven Approach for Automatically Generating Heterogenous Processors}
%\author{}
%\author{Mark Gahagan\\UC San Diego\\mgahagan@cs.ucsd.edu\and Jack Sampson\\UC San Diego\\jsampson@cs.ucsd.edu\and Vinicius Petrucci\\UC San Diego\\vpetrucci@cs.ucsd.edu\and Lingjia Tang\\UC San Diego\\lingjia@cs.ucsd.edu\and Jason Mars\\UC San Diego\\mars@cs.ucsd.edu}
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\begin{abstract}
Heterogeneous CMPs with two types of cores are now available as
commodity products, and recent proposals argue for increasingly
heterogeneous CMPs as a means to continue to improve
energy efficiency. However, both designing and exploiting increasingly
heterogeneous processors raises many challenges. Design choices for
resource heterogeneity span a vast design space and must balance
diversity commensurate with workload variability without becoming
intractable to verify and construct. At run time, scheduling
applications on the appropriate core is key to profiting from
heterogeneity.

In this paper, we present \Ravan{}, an analytical model for tailoring
heterogeneous processor resources to a target workload based on
architecture-independent properties of the applications. This is done by
pre-selecting the architecture features that can be adjusted as well as
selecting the range of those adjustments. We show that
8 primary features are sufficient to guide both core feature
selection and runtime placement. Using \Ravan{}, we produce heterogeneous
platforms that are an average of 10\% more energy efficient than comparable
two-way heterogeneous platforms and homogeneous processors alike.
\end{abstract}

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%\section*{Acknowledgements}


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